Application of Electrical Prospecting in Cave Detection in Loess Area

The settlement calculation of postgrouting piles is complex and depends on the calculation method and parameters. Static load tests were conducted to compare the settlement characteristics of nongrouting and postgrouting piles, and three vital parameters in the layer-wise summation method were revised to predict the settlement of postgrouting piles. The elastic compression coefficient was deduced based on the Mindlin–Geddes method by considering the influence of the change in the pile side resistance distribution and end resistance ratio on the elastic compression after grouting. The relationship between the compression modulus and soil gravity stress and cone penetration resistance were established, respectively, using experimental data. The optimum value of the settlement empirical coefficient was determined using regional data. Finally, we used the postgrouting pile of the Wuqi–Dingbian expressway as a practical example. The results obtained from the layer-wise summation method after parametric optimization were close to the measured values. The results of this study provide reference data and guidance for the settlement calculation of postgrouting piles in this area.

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MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3454009 ◽

2021 ◽

Vol 17 (2s) ◽

pp. 1-11

Author(s):

Xiaochuan Tang ◽

Mingzhe Liu ◽

Hao Zhong ◽

Yuanzhen Ju ◽

Weile Li ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Large Scale ◽

Remote Sensing Image ◽

Disaster Risk ◽

Multiple Instance Learning ◽

Remote Sensing Images ◽

Loess Area ◽

Remote Sensing Image Classification ◽

Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

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Determining deep root water uptake patterns with tree age in the Chinese loess area

Agricultural Water Management ◽

10.1016/j.agwat.2021.106810 ◽

2021 ◽

Vol 249 ◽

pp. 106810

Author(s):

Ze Tao ◽

Eric Neil ◽

Bingcheng Si

Keyword(s):

Water Uptake ◽

Root Water Uptake ◽

Tree Age ◽

Chinese Loess ◽

Loess Area ◽

Deep Root ◽

Water Uptake Patterns

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Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

Remote Sensing ◽

10.3390/rs13153044 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3044

Author(s):

Mingjie Liao ◽

Rui Zhang ◽

Jichao Lv ◽

Bin Yu ◽

Jiatai Pang ◽

...

Keyword(s):

Time Series ◽

Large Scale ◽

Ground Deformation ◽

Dynamic Change ◽

Deformation Monitoring ◽

Chinese Loess Plateau ◽

Ground Subsidence ◽

Shanxi Province ◽

Environment Change ◽

Loess Area

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.

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